Modeling the effects of introducing a new antibiotic in a hospital setting: A case study

Abstract

The increase in antibiotic resistance continues to pose a public health risk as very few new antibiotics are being produced, and bacteria resistant to currently prescribed antibiotics is growing. Within a typical hospital setting, one may find patients colonized with bacteria resistant to a single antibiotic, or, of a more emergent threat, patients may be colonized with bacteria resistant to multiple antibiotics. Precautions have been implemented to try to prevent the growth and spread of antimicrobial resistance such as a reduction in the distribution of antibiotics and increased hand washing and barrier preventions; however, the rise of this resistance is still evident. As a result, there is a new movement to try to re-examine the need for the development of new antibiotics. In this paper, we use mathematical models to study the possible benefits of implementing a new antibiotic in this setting; through these models, we examine the use of a new antibiotic that is distributed in various ways and how this could reduce total resistance in the hospital. We compare several different models in which patients colonized with both single and dual-resistant bacteria are present, including a model with no additional treatment protocols for the population colonized with dual-resistant bacteria as well as models including isolation and/or treatment with a new antibiotic. We examine the benefits and limitations of each scenario in the simulations presented.

Antibiotic Restriction Might Facilitate the Emergence of Multi-drug Resistance

“…Recent theoretical studies support these claims: Wu et al have shown that maintaining variation in stockpiled influenza drugs might substantially reduce resistance [ 21 ], while Joyner et al demonstrated the benefits of developing new antibiotics and expanding the range of possible antimicrobial treatments [ 22 ].…”

What should be considered if you decide to build your own mathematical model for predicting the development of bacterial resistance? Recommendations based on a systematic review of the literature

“…We reviewed only models where AB consumption was introduced as an independent parameter, because we consider the process of developing resistance as a function of time and antibiotic consumption. That is why some papers were excluded at the stage of full text analysis (Bergstrom et al, 2004 ; Miller et al, 2004 ; Boni and Feldman, 2005 ; Huovinen, 2005 ; Mera, 2005 ; Reluga, 2005 ; Webb et al, 2005 ; Levin and Rozen, 2006 ; Schinazi, 2006 ; D'Agata et al, 2007 ; Zalounina et al, 2007 ; Drovandi and Pettitt, 2008 ; Pienaar et al, 2009 ; Artalejo et al, 2010 ; Haber et al, 2010 ; Kardas-Sloma et al, 2011 ; Kouyos et al, 2011 ; Opatowski et al, 2011 ; Wang et al, 2011 ; Banks et al, 2012 ; Chamchod and Ruan, 2012a , b ; Deng et al, 2012 ; Hurford et al, 2012 ; Joyner et al, 2012 ; Sotto and Lavigne, 2012 ; Yahdi et al, 2012 ; Horsburgh et al, 2013 ; Lee et al, 2013a , b ; Worby et al, 2013 ). The seven remaining models were classified according to the type of link between resistance and AB consumption.…”

Applications of pharmacometrics in the clinical development and pharmacotherapy of anti-infectives

“…Pharmacometrics has in this context arisen as a formidable tool to bridge and link between key concepts in these disciplines and to provide a rationale, scientific framework throughout the drug development continuum as well as in clinical applications [1,2]. There are case studies which suggest that basing optimization of treatment on pharmacometric assessments improves the risk-management of MDR pathogens in the clinical realm by minimizing the failures and mistakes associated with inappropriate or careless use of antibiotic therapies [3,4]. …”